class_docstr: A data iterator object to contain a dynamic graph with a
  changing edge set and weights . The feature set and node labels
  (target) are also dynamic. The iterator returns a single discrete temporal
  snapshot for a time period (e.g. day or week). This single snapshot is a
  Pytorch Geometric Data object. Between two temporal snapshots the edges,
  edge weights, target matrices and optionally passed attributes might change.
  
  Args:
      edge_indices (Sequence of Numpy arrays): Sequence of edge index tensors.
      edge_weights (Sequence of Numpy arrays): Sequence of edge weight tensors.
      features (Sequence of Numpy arrays): Sequence of node feature tensors.
      targets (Sequence of Numpy arrays): Sequence of node label (target) tensors.
      **kwargs (optional Sequence of Numpy arrays): Sequence of additional attributes.
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code: def __init__(
      self,
      edge_indices: Edge_Indices,
      edge_weights: Edge_Weights,
      features: Node_Features,
      targets: Targets,
      **kwargs: Additional_Features,
  ):
      self.edge_indices = edge_indices
      self.edge_weights = edge_weights
      self.features = features
      self.targets = targets
      self.additional_feature_keys = []
      for key, value in kwargs.items():
          setattr(self, key, value)
          self.additional_feature_keys.append(key)
      self._check_temporal_consistency()
      self._set_snapshot_count()
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/signal/dynamic_graph_temporal_signal.py
imports: import torch
  import numpy
  from typing import Sequence, Union
  from torch_geometric.data import Data
label_desc: Use image node_id 1 to create a new DynamicGraphTemporalSignal object from inherited base classes: object with example: obj = DynamicGraphTemporalSignal(edge_indices, edge_weights, features, targets)